Reinforcement Learning
OnlineRobustReinforcementLearningwithModel Uncertainty
Robust reinforcement learning (RL) is to find a policy that optimizes the worstcase performance over an uncertainty set of MDPs. In this paper, we focus on model-freerobust RL, where the uncertainty set is defined to be centering at a misspecified MDP that generates a single sample trajectory sequentially, and is assumed to beunknown.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
Finite-Time Analysis of Single-Timescale Actor-Critic
Actor-critic methods have achieved significant success in many challenging applications.
Finite-Time Analysis of Single-Timescale Actor-Critic
Actor-critic methods have achieved significant success in many challenging applications.
BooVI: ProvablyEfficientBootstrappedValue Iteration
In this paper, we develop a variant of bootstrapped LSVI,namely BooVI, which bridges such agapbetween practice andtheory.
Meta-ReinforcementLearningwith Self-ModifyingNetworks
However, these neural systems are slow learners producing specialized agentswithnomechanism tocontinue learning beyondtheirtrainingcurriculum.
